Search results for: choice experiments (CE)

Authors: Rotimi Larayetana, Yahaya Abdulrazaq, Oladunni O. Falola, Abayomi Ajayi

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The aim of this study was to perform green synthesis of silver nanoparticles (AgNPs) with the aqueous extract of Moringa oleifera Lam (M oleifera) leaves and determine its effects on benign prostatic hyperplasia in Wistar rats. Silver nitrate (AgNO₃) solution was reduced using the aqueous extract of Moringa oleifera Lam leaves, the resultant biogenic AgNPs were characterized by Fourier transformed infrared spectrophotometric, SEM, TEM and X-ray diffraction analysis. Animal experiments involved thirty (30) adult male Wistar rats randomly divided into five groups (A to E; n ₌ 5). Group A received only subcutaneous injection of olive oil daily while the other groups got 3 mg/kg/daily of testosterone propionate (TP) subcutaneously plus 50 mg/kg/daily of AgNPs intraperitoneally (B), 3 mg/kg/daily of TP plus 25 mg/kg/daily of AgNPs (C), 3 mg/kg/daily of TP only (D) and 25 mg/kg/daily of AgNPs only (E). The animals were sacrificed after 14 days, and the prostate gland, liver, and kidney were processed for histological analysis. Phytochemical screening and GC-MS analysis were performed to determine the composition of the M oleifera extract used. Biogenic AgNPs with an average diameter of 23 nm were synthesized. Biogenic AgNPs ameliorated hormone-induced prostate enlargement, and the inhibition of prostatic hypertrophy could be due to the presence of a significant amount of plant fatty acids and phytosterols in the aqueous extract of M oleifera extract. However, the administration of biogenic AgNPs at higher doses impacted negatively on the cytoarchitecture of the liver. Green synthesis of AgNPs with the aqueous extract of Moringa oleifera might be beneficial for the treatment of BPH.

Keywords: benign prostatic hyperplasia, biogenic synthesis, Moringa oleifera, silver nanoparticles, testosterone

Procedia PDF Downloads 88

Authors: Faisal Islam, J. Ramkumar

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The applications of composite materials in aerospace, sporting and automotive industries need high quality machined surfaces and dimensional accuracy. Some studies have been done to understand the fiber failure mechanisms encountered during milling machining of CFRP composites but none are capable of explaining the exact nature of the orientation-based fiber failure mechanisms encountered in the milling machining process. The objective of this work is to gain a better understanding of the orientation-based fiber failure mechanisms occurring on the slot edges during CFRP milling machining processes. The occurrence of damage is predicted by a schematic explanation based on the mechanisms of material removal which in turn depends upon fiber cutting angles. A geometric model based on fiber cutting angle and fiber orientation angle is proposed that defines the critical and safe zone during machining and predicts the occurrence of delamination. Milling machining experiments were performed on composite samples of varying fiber orientations to verify the proposed theory. Mean fiber pulled out length was measured from the microscopic images of the damaged area to quantify the amount of damage produced. By observing the damage occurring for different fiber orientation angles and fiber cutting angles for up-milling and down-milling edges and correlating it with the material removal mechanisms as described earlier, it can be concluded that the damage/delamination mainly depends on the portion of the fiber cutting angles that lies within the critical cutting angle zone.

Keywords: unidirectional composites, milling, machining damage, delamination, carbon fiber reinforced plastics (CFRPs)

Procedia PDF Downloads 528

Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

Procedia PDF Downloads 147

Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

Procedia PDF Downloads 245

Authors: Jichi Wu

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The dwindling of resources requires a more sustainable design. New technology could bring new materials and processing techniques to the fashion industry and push it to a more sustainable future. Thus this paper explores cutting-edge researches on the life-cycle of closed-loop products and aims to find innovative ways to recycle and upcycle. For such a goal, the author investigated how low utilization plants and leftover fiber could be turned into ecological textiles in fashion. Through examining the physical and chemical properties (cellulose content/ fiber form) of ecological textiles to explore their wearability, this paper analyzed the prospect of bio-fabrics (weavable plants) in body-oriented fashion design and their potential in sustainable fashion and textile design. By extracting cellulose from 9 different types or sections of plants, the author intends to find an appropriate method (such as ion solution extraction) to mostly increase the weavability of plants, so raw materials could be more effectively changed into fabrics. All first-hand experiment data were carefully collected and then analyzed under the guidance of related theories. The result of the analysis was recorded in detail and presented in an understandable way. Various research methods are adopted through this project, including field trip and experiments to make comparisons and recycle materials. Cross-discipline cooperation is also conducted for related knowledge and theories. From this, experiment data will be collected, analyzed, and interpreted into a description and visualization results. Based on the above conclusions, it is possible to apply weavable plant fibres to develop new textile and fashion.

Keywords: wearable bio-textile, sustainability, economy, ecology, technology, weavability, fashion design

Procedia PDF Downloads 142

Authors: Onur Kaya, Halit Bayer

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Global grocery and supermarket sales are among the largest markets in the world and perishable products such as fresh produce, dairy and meat constitute the biggest section of these markets. Due to their deterioration over time, the demand for these products depends highly on their freshness. They become totally obsolete after a certain amount of time causing a high amount of wastage and decreases in grocery profits. In addition, customers are asking for higher product variety in perishable product categories, leading to less predictable demand per product and to more out-dating. Effective management of these perishable products is an important issue since it is observed that billions of dollars’ worth of food is expired and wasted every month. We consider coordinated inventory and pricing decisions for perishable products with a time and price dependent random demand function. We use stochastic dynamic programming to model this system for both periodically-reviewed and continuously-reviewed inventory systems and prove certain structural characteristics of the optimal solution. We prove that the optimal ordering decision scenario has a monotone structure and the optimal price value decreases by time. However, the optimal price changes in a non-monotonic structure with respect to inventory size. We also analyze the effect of 1 different parameters on the optimal solution through numerical experiments. In addition, we analyze simple-to-implement heuristics, investigate their effectiveness and extract managerial insights. This study gives valuable insights about the management of perishable products in order to decrease wastage and increase profits.

Keywords: age-dependent demand, dynamic programming, perishable inventory, pricing

Procedia PDF Downloads 244

Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

Procedia PDF Downloads 131

Authors: Jemima C. A. Sumboh

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Given the urgency and consensus for countries to move towards Universal Health Coverage (UHC), health financing systems need to be accurately and consistently monitored to provide valuable data to inform policy and practice. Most of the indicators for monitoring UHC, particularly catastrophe and impoverishment, are established based on the impact of out-of-pocket health payments (OOPHP) on households’ living standards, collected through varied household surveys. These surveys, however, vary substantially in survey methods such as the length of the recall period or the number of items included in the survey questionnaire or the farming of questions, potentially influencing the level of OOPHP. Using different survey instruments can provide inaccurate, inconsistent, erroneous and misleading estimates of UHC, subsequently influencing wrong policy decisions. Using data from a household budget survey conducted by the Navrongo Health Research Center in Ghana from May 2017 to December 2018, this study intends to explore the potential implications of using surveys with varied levels of disaggregation of OOPHP data on estimates of financial risk protection. The household budget survey, structured around food and non-food expenditure, compared three OOPHP measuring instruments: Version I (existing questions used to measure OOPHP in household budget surveys), Version II (new questions developed through benchmarking the existing Classification of the Individual Consumption by Purpose (COICOP) OOPHP questions in household surveys) and Version III (existing questions used to measure OOPHP in health surveys integrated into household budget surveys- for this, the demographic and health surveillance (DHS) health survey was used). Version I, II and III contained 11, 44, and 56 health items, respectively. However, the choice of recall periods was held constant across versions. The sample size for Version I, II and III were 930, 1032 and 1068 households, respectively. Financial risk protection will be measured based on the catastrophic and impoverishment methodologies using STATA 15 and Adept Software for each version. It is expected that findings from this study will present valuable contributions to the repository of knowledge on standardizing survey instruments to obtain estimates of financial risk protection that are valid and consistent.

Keywords: Ghana, household budget surveys, measuring financial risk protection, out-of-pocket health payments, survey instruments, universal health coverage

Procedia PDF Downloads 132

Authors: Zhang Songning, Guan Zheng, Ci Yan, Ding Gangyi

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The application of physical simulation engines in collaborative editing systems has an important background and role. Firstly, physical simulation engines can provide real-world physical simulations, enabling users to interact and collaborate in real time in virtual environments. This provides a more intuitive and immersive experience for collaborative editing systems, allowing users to more accurately perceive and understand various elements and operations in collaborative editing. Secondly, through physical simulation engines, different users can share virtual space and perform real-time collaborative editing within it. This real-time sharing and collaborative editing method helps to synchronize information among team members and improve the efficiency of collaborative work. Through experiments, the average model transmission speed of a single person in the collaborative editing system has increased by 141.91%; the average model processing speed of a single person has increased by 134.2%; the average processing flow rate of a single person has increased by 175.19%; the overall efficiency improvement rate of a single person has increased by 150.43%. With the increase in the number of users, the overall efficiency remains stable, and the physical simulation engine running status collaborative editing system also has horizontal scalability. It is not difficult to see that the design and implementation of a collaborative editing system based on physical simulation engines not only enriches the user experience but also optimizes the effectiveness of team collaboration, providing new possibilities for collaborative work.

Keywords: physics engine, simulation technology, collaborative editing, system design, data transmission

Procedia PDF Downloads 75

Authors: Alisher Ibragimov, Sofya Senotrusova, Aleksandra Beliaeva, Egor Ushakov, Yuri Markin

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Mammography plays a critical role in screening for breast cancer in women, and artificial intelligence has enabled the automatic detection of diseases in medical images. Many of the current techniques used for mammogram analysis focus on a single view (mediolateral or craniocaudal view), while in clinical practice, radiologists consider multiple views of mammograms from both breasts to make a correct decision. Consequently, computer-aided diagnosis (CAD) systems could benefit from incorporating information gathered from multiple views. In this study, the introduce a method based on a Siamese neural network (SNN) model that simultaneously analyzes mammographic images from tri-view: bilateral and ipsilateral. In this way, when a decision is made on a single image of one breast, attention is also paid to two other images – a view of the same breast in a different projection and an image of the other breast as well. Consequently, the algorithm closely mimics the radiologist's practice of paying attention to the entire examination of a patient rather than to a single image. Additionally, to the best of our knowledge, this research represents the first experiments conducted using the recently released Vietnamese dataset of digital mammography (VinDr-Mammo). On an independent test set of images from this dataset, the best model achieved an AUC of 0.87 per image. Therefore, this suggests that there is a valuable automated second opinion in the interpretation of mammograms and breast cancer diagnosis, which in the future may help to alleviate the burden on radiologists and serve as an additional layer of verification.

Keywords: breast cancer, computer-aided diagnosis, deep learning, multi-view mammogram, siamese neural network

Procedia PDF Downloads 130

Authors: Der Chyan Lin

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Since the early introduction by Ormondroyd and Den Hartog, vibration absorber (VA) has become one of the most commonly used vibration mitigation strategies. The strategy is most effective for a primary plant subjected to a single frequency excitation. For continuous systems, notable advances in vibration absorption in the multi-frequency system were made. However, the efficacy of the VA strategy for systems under multi-frequency excitation is not well understood. For example, for an N degrees-of-freedom (DOF) primary-absorber system, there are N 'peak' frequencies of large amplitude vibration per every new excitation frequency. In general, the usable range for vibration absorption can be greatly reduced as a result. Frequency modulated harmonic excitation is a commonly seen multi-frequency excitation example: f(t) = cos(ϖ(t)t) where ϖ(t)=ω(1+α sin⁡(δt)). It is known that f(t) has a series expansion given by the Bessel function of the first kind, which implies an infinity of forcing frequencies in the frequency modulated harmonic excitation. For an SDOF system of natural frequency ωₙ subjected to f(t), it can be shown that amplitude peaks emerge at ω₍ₚ,ₖ₎=(ωₙ ± 2kδ)/(α ∓ 1),k∈Z; i.e., there is an infinity of resonant frequencies ω₍ₚ,ₖ₎, k∈Z, making the use of VA strategy ineffective. In this work, we propose an absorber frequency placement strategy for SDOF vibration systems subjected to frequency-modulated excitation. An SDOF linear mass-spring system coupled to lateral absorber systems is used to demonstrate the ideas. Although the mechanical components are linear, the governing equations for the coupled system are nonlinear. We show using N identical absorbers, for N ≫ 1, that (a) there is a cluster of N+1 natural frequencies around every natural absorber frequency, and (b) the absorber frequencies can be moved away from the plant's resonance frequency (ω₀) as N increases. Moreover, we also show the bandwidth of the VA performance increases with N. The derivations of the clustering and bandwidth widening effect will be given, and the superiority of the proposed strategy will be demonstrated via numerical experiments.

Keywords: Bessel function, bandwidth, frequency modulated excitation, vibration absorber

Procedia PDF Downloads 148

Authors: Lucas Caldas, Pablo Paulse, Karla Hora

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Shipping containers are present in different Brazilian cities, firstly used for transportation purposes, but which become waste materials and an environmental burden in their end-of-life cycle. In the last decade, in Brazil, some buildings made partly or totally from shipping containers started to appear, most of them for commercial and office uses. Although the use of a reused container for buildings seems a sustainable solution, it is very important to measure the thermal and energy aspects when they are used as such. In this context, this study aims to evaluate the thermal and energy performance of an office building totally made from a 12-meter-long, High Cube 40’ shipping container in different Brazilian Bioclimatic Zones. Four different constructive solutions, mostly used in Brazil were chosen: (1) container without any covering; (2) with internally insulated drywall; (3) with external fiber cement boards; (4) with both drywall and fiber cement boards. For this, the DesignBuilder with EnergyPlus was used for the computational simulation in 8760 hours. The EnergyPlus Weather File (EPW) data of six Brazilian capital cities were considered: Curitiba, Sao Paulo, Brasilia, Campo Grande, Teresina and Rio de Janeiro. Air conditioning appliance (split) was adopted for the conditioned area and the cooling setpoint was fixed at 25°C. The coefficient of performance (CoP) of air conditioning equipment was set as 3.3. Three kinds of solar absorptances were verified: 0.3, 0.6 and 0.9 of exterior layer. The building in Teresina presented the highest level of energy consumption, while the one in Curitiba presented the lowest, with a wide range of differences in results. The constructive option of external fiber cement and drywall presented the best results, although the differences were not significant compared to the solution using just drywall. The choice of absorptance showed a great impact in energy consumption, mainly compared to the case of containers without any covering and for use in the hottest cities: Teresina, Rio de Janeiro, and Campo Grande. This study brings as the main contribution the discussion of constructive aspects for design guidelines for more energy-efficient container buildings, considering local climate differences, and helps the dissemination of this cleaner constructive practice in the Brazilian building sector.

Keywords: bioclimatic zones, Brazil, shipping containers, thermal and energy performance

Procedia PDF Downloads 169

Authors: Kateryna Zhdanova, Evelyn Szeinbaum, Michelle Lo, Yeonjae Jo, Abel E. Navarro

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Alginate hydrogel beads (AB), spent peppermint leaf (PM), and a hybrid adsorbent of these two materials (ABPM) were studied as potential biosorbents of Cobalt (II) ions from aqueous solutions. Cobalt ion is a commonly underestimated pollutant that is responsible for several health problems. Discontinuous batch experiments were conducted at room temperature to evaluate the effect of solution acidity, mass of adsorbent on the adsorption of Co(II) ions. The interfering effect of salinity, the presence of surfactants, an organic dye, and Pb(II) ions were also studied to resemble the application of these adsorbents in real wastewater. Equilibrium results indicate that Co(II) uptake is maximized at pH values higher than 5, with adsorbent doses of 200 mg, 200 mg, and 120 mg for AB, PM, and ABPM, respectively. Co(II) adsorption followed the trend AB > ABPM > PM with Adsorption percentages of 77%, 71% and 64%, respectively. Salts had a strong negative effect on the adsorption due to the increase of the ionic strength and the competition for adsorption sites. The presence of Pb(II) ions, surfactant, and dye BY57 had a slightly negative effect on the adsorption, apparently due to their interaction with different adsorption sites that do not interfere with the removal of Co(II). A polar-electrostatic adsorption mechanism is proposed based on the experimental results. Scanning electron microscopy indicates that adsorbent has appropriate morphological and textural properties, and also that ABPM encapsulated most of the PM inside of the hydrogel beads. These experimental results revealed that AB, PM, and ABPM are promising adsorbents for the elimination of Co(II) ions from aqueous solutions under different experimental conditions. These biopolymers are proposed as eco-friendly alternatives for the removal of heavy metal ions at lower costs than the conventional techniques.

Keywords: adsorption, Co(II) ions, alginate hydrogel beads, spent peppermint leaf, pH

Procedia PDF Downloads 125

Authors: Shu-Jen Chen, Yi-Chien Wan, Ruey-Chi Wang

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Hydroxyapatite (Ca10(PO4)6(OH)2, HAp) can be applied to the fabrication of bone replacement materials, the composite of dental filling, and the adsorption of biomolecules and dyes. The integration of HAp and magnetic materials would offer several advantages for bio-separation process because the magnetic adsorbents is capable of recovered by applied magnetic field. C-phycocyanin (C-PC) and Allophycocyanin (APC), isolated from Spirulina platensis, can be used in fluorescent labeling probes, health care foods and clinical diagnostic reagents. Although the purification of C-PC and APC are reported by HAp adsorption, the adsorption of C-PC and APC by magnetic HAp composites was not reported yet. Therefore, the fabrication of HAp with magnetic silica nanoparticles for proteins adsorption was investigated in this work. First, the magnetic silica particles were prepared by covering silica layer on Fe3O4 nanoparticles with a reverse micelle method. Then, the Fe3O4@SiO2 nanoparticles were mixed with calcium carbonate to obtain magnetic silica/calcium carbonate composites (Fe3O4@SiO2/CaCO3). The Fe3O4@SiO2/CaCO3 was further reacted with K2HPO4 for preparing the magnetic silica/hydroxyapatite composites (Fe3O4@SiO2/HAp). The adsorption experiments indicated that the adsorption capacity of Fe3O4@SiO2/HAp toward C-PC and APC were highest at pH 6. The adsorption of C-PC and APC by Fe3O4@SiO2/HAp could be correlated by the pseudo-second-order model, indicating chemical adsorption dominating the adsorption process. Furthermore, the adsorption data showed that the adsorption of Fe3O4@SiO2/HAp toward C-PC and APC followed the Langmuir isotherm. The isoelectric points of C-PC and APC were around 5.0. Additionally, the zeta potential data showed the Fe3O4@SiO2/HAp composite was negative charged at pH 6. Accordingly, the adsorption mechanism of Fe3O4@SiO2/HAp toward C-PC and APC should be governed by hydrogen bonding rather than electrostatic interaction. On the other hand, as compared to C-PC, the Fe3O4@SiO2/HAp shows higher adsorption affinity toward APC. Although the Fe3O4@SiO2/HAp cannot recover C-PC and APC from Spirulina platensis homogenate, the Fe3O4@SiO2/HAp can be applied to separate C-PC and APC.

Keywords: hydroxyapatite, magnetic, C-phycocyanin, allophycocyanin

Procedia PDF Downloads 148

Authors: Veronika Lesáková, Silvia Slezáková, František Štěpánek

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Additive manufacturing technologies producing drug dosage forms aimed at personalized medicine applications are promising strategies with several advantages over the conventional production methods. One of the emerging technologies is 3D printing which reduces manufacturing steps and thus allows a significant drop in expenses. A decrease in material consumption is also a highly impactful benefit as the tested drugs are frequently expensive substances. In addition, 3D printed dosage forms enable increased patient compliance and prevent misdosing as the dosage forms are carefully designed according to the patient’s needs. The incorporation of multiple drugs into a single dosage form further increases the degree of personalization. Our research focuses on the development of 3D printed tablets incorporating multiple drugs (candesartan, losartan) and thermoplastic polymers (e.g., KlucelTM HPC EF). The filaments, an essential feed material for 3D printing,wereproduced via hot-melt extrusion. Subsequently, the extruded filaments of various formulations were 3D printed into tablets using an FDM 3D printer. Then, we have assessed the influence of the internal structure of 3D printed tablets and formulation on dissolution behaviour by obtaining the dissolution profiles of drugs present in the 3D printed tablets. In conclusion, we have developed tablets containing multiple drugs providing modified release profiles. The 3D printing experiments demonstrate the high tunability of 3D printing as each tablet compartment is constructed with a different formulation. Overall, the results suggest that the 3D printing technology is a promising manufacturing approach to dual tablet preparation for personalized medicine.

Keywords: 3D printing, drug delivery, hot-melt extrusion, dissolution kinetics

Procedia PDF Downloads 164

Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

Procedia PDF Downloads 73

Authors: Hassan Sbiga

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Laboratory measurements methods were undertaken on core samples selected from three different fields (A, B, and C) from the Nubian Sandstone Formation of the central graben reservoirs in Libya. These measurements were conducted in order to determine the factors which affect resistivity parameters, and to investigate the effect of rock heterogeneity and wettability on these parameters. This included determining the saturation exponent (n) in the laboratory at two stages. The first stage was before wettability measurements were conducted on the samples, and the second stage was after the wettability measurements in order to find any effect on the saturation exponent. Another objective of this work was to quantify experimentally pores and porosity types (macro- and micro-porosity), which have an affect on the electrical properties, by integrating capillary pressure curves with other routine and special core analysis. These experiments were made for the first time to obtain a relation between pore size distribution and saturation exponent n. Changes were observed in the formation resistivity factor and cementation exponent due to ambient conditions and changes of overburden pressure. The cementation exponent also decreased from GHE-5 to GHE-8. Changes were also observed in the saturation exponent (n) and water saturation (Sw) before and after wettability measurement. Samples with an oil-wet tendency have higher irreducible brine saturation and higher Archie saturation exponent values than samples with an uniform water-wet surface. The experimental results indicate that there is a good relation between resistivity and pore type depending on the pore size. When oil begins to penetrate micro-pore systems in measurements of resistivity index versus brine saturation (after wettability measurement), a significant change in slope of the resistivity index relationship occurs.

Keywords: part of thesis, cementation, wettability, resistivity

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Authors: Kayondoamunmose Kamafrika

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Experimental based Engineering education approach plays a vital role in the development of student’s deep understanding of both social and physical sciences. Experimental based education approach through laboratory class activities prepare students to meet national demand for high-tech skilled individuals in the government and private sector. However, students across the country are faced with difficulties in classes that involve laboratory activities: poor experimental based exposure in their early development of student’s education-life-cycle, lack of student engagement in scientific method practical thinking approach, lack of communication between students and the instructor during class, a large number of students in one classroom, lack of instruments and improper equipment calibration. The purpose of this paper is to help students develop their own scientific knowledge and understanding, develop their methodologies in the design of experiments, collect and analyze data, write laboratory reports, present and explain their findings. Experimental based laboratory activities allow students to learn with high-level understanding as well as engage in the design processes of constructing knowledge through practical means of doing science. Experimental based education systems approach will act as a catalyst in the development of practical-based-educational methodologies in social and physical science and engineering domain of learning; thereby, converting laboratory classes into pilot industries and students into professional experts in finding a solution for complex problems, research, and development of super high- tech systems.

Keywords: experimental, engineering, innovation, practicability

Procedia PDF Downloads 182

Authors: Min Chen, Rubi Gupta

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Understanding and predicting stock market movements is a challenging problem. It is believed stock markets are partially driven by public sentiments, which leads to numerous research efforts to predict stock market trend using public sentiments expressed on social media such as Twitter but with limited success. Recently a microblogging website StockTwits is becoming increasingly popular for users to share their discussions and sentiments about stocks and financial market. In this project, we analyze the text content of StockTwits tweets and extract financial sentiment using text featurization and machine learning algorithms. StockTwits tweets are first pre-processed using techniques including stopword removal, special character removal, and case normalization to remove noise. Features are extracted from these preprocessed tweets through text featurization process using bags of words, N-gram models, TF-IDF (term frequency-inverse document frequency), and latent semantic analysis. Machine learning models are then trained to classify the tweets' sentiment as positive (bullish) or negative (bearish). The correlation between the aggregated daily sentiment and daily stock price movement is then investigated using Pearson’s correlation coefficient. Finally, the sentiment information is applied together with time series stock data to predict stock price movement. The experiments on five companies (Apple, Amazon, General Electric, Microsoft, and Target) in a duration of nine months demonstrate the effectiveness of our study in improving the prediction accuracy.

Keywords: machine learning, sentiment analysis, stock price prediction, tweet processing

Procedia PDF Downloads 149

Authors: Ping Tan, Xiaomeng Su, Yi Shen

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The motion intention in the motor imagery braincomputer interface is identified by classifying the event-related desynchronization (ERD) and event-related synchronization ERS characteristics of sensorimotor rhythm (SMR) in EEG signals. When the subject imagines different limbs or different parts moving, the rhythm components and bandwidth will change, which varies from person to person. How to find the effective sensorimotor frequency band of subjects is directly related to the classification accuracy of brain-computer interface. To solve this problem, this paper proposes a Minimum Distance to Riemannian Mean Classification method based on Non-Uniform Filter Banks. During the training phase, the EEG signals are decomposed into multiple different bandwidt signals by using multiple band-pass filters firstly; Then the spatial covariance characteristics of each frequency band signal are computered to be as the feature vectors. these feature vectors will be classified by the MDRM (Minimum Distance to Riemannian Mean) method, and cross validation is employed to obtain the effective sensorimotor frequency bands. During the test phase, the test signals are filtered by the bandpass filter of the effective sensorimotor frequency bands, and the extracted spatial covariance feature vectors will be classified by using the MDRM. Experiments on the BCI competition IV 2a dataset show that the proposed method is superior to other classification methods.

Keywords: non-uniform filter banks, motor imagery, brain-computer interface, minimum distance to Riemannian mean

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Authors: A. Katsman, L. Bloch, Y. Etinger, Y. Kauffmann, B. Pokroy

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Various nanosized amorphous alumina thin films in the range of (2.4 - 63.1) nm were deposited onto amorphous carbon and amorphous Si3N4 membrane grids. Transmission electron microscopy (TEM), electron energy loss spectroscopy (EELS), X-ray photoelectron spectroscopy (XPS) and differential scanning calorimetry (DSC) techniques were used to probe the size effect on the short range order and the amorphous to crystalline phase transition temperature. It was found that the short-range order changes as a function of size: the fraction of tetrahedral Al sites is greater in thinner amorphous films. This result correlates with the change of amorphous alumina density with the film thickness demonstrated by the reflectivity experiments: the thinner amorphous films have the less density. These effects are discussed in terms of surface reconstruction of the amorphous alumina films. The average atomic binding energy in the thin film layer decreases with decease of the thickness, while the average O-Al interatomic distance increases. The reconstruction of amorphous alumina is induced by the surface reconstruction, and the short range order changes being dependent on the density. Decrease of the surface energy during reconstruction is the driving force of the alumina reconstruction (density change) followed by relaxation process (short range order change). The amorphous to crystalline phase transition temperature measured by DSC rises with the decrease in thickness from 997.6°C for 13.9 nm to 1020.4 °C for 2.7 nm thick. This effect was attributed to the different film densities: formation of nanovoids preceding and accompanying crystallization process influences the crystallization rate, and by these means, the temperature of crystallization peak.

Keywords: amorphous alumina, density, short range order, size effect

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Authors: Mohammad Khalid, Mariya Munir, Jacelyn Rice Boyaue

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Highways are considered a major source of pollution to storm-water, and its runoff can introduce various contaminants, including nutrients, Indicator bacteria, heavy metals, chloride, and phosphorus compounds, which can have negative impacts on receiving waters. This study assessed the ability of biochar for contaminants removal and to improve the water holding capacity of soil biochar mixture. For this, ten commercially available biochar has been strategically selected. Lab scale batch testing was done at 3% and 6% by the weight of the soil to find the preliminary estimate of contaminants removal along with hydraulic conductivity and water retention capacity. Furthermore, from the above-conducted studies, six best performing candidate and an application rate of 6% has been selected for the column studies. Soil biochar mixture was filled in 7.62 cm assembled columns up to a fixed height of 76.2 cm based on hydraulic conductivity. A total of eight column experiments have been conducted for nutrient, heavy metal, and indicator bacteria analysis over a period of one year, which includes a drying as well as a deicing period. The saturated hydraulic conductivity was greatly improved, which is attributed to the high porosity of the biochar soil mixture. Initial data from the column testing shows that biochar may have the ability to significantly remove nutrients, indicator bacteria, and heavy metals. The overall study demonstrates that biochar could be efficiently applied with clay soil to improve the soil's hydraulic characteristics as well as remove the pollutants from the stormwater runoff.

Keywords: biochar, nutrients, indicator bacteria, storm-water treatment, sustainability

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Authors: Bummo Ahn

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Palpation is widely used to measure soft tissue firmness or stiffness in the living condition in order to apply detection, diagnosis, and treatment of tumors, scar tissue, abnormal muscle tone, or muscle spasticity. Since these methods are subjective and depend on the proficiency level, it is concluded that there are other diagnoses depending on the condition of the experts and the results are not objective. The mechanical property obtained by using the elasticity of the tissue is important to calculate a predictive variable for monitoring abnormal tissues. If the mechanical load such as reaction force on the foot increases in the same region under the same conditions, the mechanical property of the tissue is changed. Therefore, objective diagnosis is possible not only for experts but also for patients using this quantitative information. Furthermore, the portable system also allows non-experts to easily diagnose at home, not in hospitals or institutions. In this paper, we introduce a portable palpation system that can be used to measure the mechanical properties of human tissue, which can be applied to monitor diabetic foot ulceration patients with measuring the mechanical property change of foot tissue. The system was designed to be smaller and portable in comparison with the conventional palpation systems. It is consists of the probe, the force sensor, linear actuator, micro control unit, the display module, battery, and housing. Using this system, we performed validation experiments by applying different palpations (3 and 5 mm) to soft tissue (silicone rubber) and measured reaction forces. In addition, we estimated the elastic moduli of the soft tissue against different palpations and compare the estimated elastic moduli that show similar value even if the palpation depths are different.

Keywords: palpation probe, portable, diabetic foot ulceration, monitoring, mechanical property

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Authors: Idit Kalisher, Israel Luski

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One result of the growing dynamicity of labor markets in recent decades is a wider scope of employment mobility – i.e., transitions between employers, either within or between careers. Employment mobility decisions are primarily affected by the current employment status of the worker, which is reflected in wage and tenure. Using 34,328 observations from the National Longitudinal Survey of Youth 1979 (NLS79), which were derived from the USA population between 1990 and 2012, this paper aims to investigate the effects of wage and tenure over employment mobility choices, and additionally to examine the effects of other personal characteristics, individual labor market characteristics and macroeconomic factors. The estimation strategy was designed to address two challenges that arise from the combination of the model and the data: (a) endogeneity of the wage and the tenure in the choice equation; and (b) unobserved heterogeneity, as the data of this research is longitudinal. To address (a), estimation was performed using two-stage limited dependent variable procedure (2SLDV); and to address (b), the second stage was estimated using femlogit – an implementation of the multinomial logit model with fixed effects. Among workers who have experienced at least one turnover, the wage was found to have a main effect on career turnover likelihood of all workers, whereas the wage effect on job turnover likelihood was found to be dependent on individual characteristics. The wage was found to negatively affect the turnover likelihood and the effect was found to vary across wage level: high-wage workers were more affected compared to low-wage workers. Tenure was found to have a main positive effect on both turnover types’ likelihoods, though the effect was moderated by the wage. The findings also reveal that as their wage increases, women are more likely to turnover than men, and academically educated workers are more likely to turnover within careers. Minorities were found to be as likely as Caucasians to turnover post wage-increase, but less likely to turnover with each additional tenure year. The wage and the tenure effects were found to vary also between careers. The difference in attitude towards money, labor market opportunities and risk aversion could explain these findings. Additionally, the likelihood of a turnover was found to be affected by previous unemployment spells, age, and other labor market and personal characteristics. The results of this research could assist policymakers as well as business owners and employers. The former may be able to encourage women and older workers’ employment by considering the effects of gender and age on the probability of a turnover, and the latter may be able to assess their employees’ likelihood of a turnover by considering the effects of their personal characteristics.

Keywords: employment mobility, endogeneity, femlogit, turnover

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Authors: Guillaume Caulier, Lola Brasseur, Patrick Flammang, Pascal Gerbaux, Igor Eeckhaut

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Saponins and quinones are two major groups of secondary metabolites widely distributed in the biosphere. More specifically, triterpenoid saponins and anthraquinones are mainly found in a wide variety of plants, bacteria and fungi. In the animal kingdom, these natural organic compounds are rare and only found in small quantities in arthropods, marine sponges and echinoderms. In this last group, triterpenoid saponins are specific to holothuroids (sea cucumbers) while anthraquinones are the chemical signature of crinoids (feather stars). Depending on the species, they present different molecular cocktails. Despite presenting different chemical properties, these molecules share numerous similarities. This study compares the biological distribution, the pharmacological effects and the ecological roles of holothuroid saponins and crinoid anthraquinones. Both of them have been defined as allomones repelling predators and parasites (i.e. chemical defense) and have interesting pharmacological properties (e.g. anti-bacterial, anti-fungal, anti-cancer). Our study investigates the chemical ecology of two symbiotic associations models; between the snapping shrimp Synalpheus stimpsonii associated with crinoids and the Harlequin crab Lissocarcinus orbicularis associated with holothuroids. Using behavioral experiments in olfactometers, chemical extractions and mass spectrometry analyses, we discovered that saponins and anthraquinones present a second ecological role: the attraction of obligatory symbionts towards their hosts. They can, therefore, be defined as kairomones. This highlights a new paradigm in marine chemical ecology: Chemical repellents are attractants to obligatory symbionts because they constitute host specific chemical signatures.

Keywords: anthraquinones, kairomones, marine symbiosis, saponins, attractant

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Authors: Matthew Conner, Leah Plocharczyk

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This study examines the role of academic libraries in support of the intellectually disabled (ID) in post-secondary education. With the growing public awareness of the ID, there has been recognition of their need for post-secondary educational opportunities. This was an unforeseen result for a population that has been associated with elementary levels of education, yet the reasons are compelling. After aging out of the school system, the ID need and deserve educational and social support as much as anyone. Moreover, the commitment to diversity in higher education rings hollow if this group is excluded. Yet, challenges remain to integrating the ID into a college curriculum. This presentation focuses on the role of academic libraries. Neglecting this vital resource for the support of the ID is not to be thought of, yet the library’s contribution is not clear. Library collections presume reading ability and libraries already struggle to meet their traditional goals with the resources available. This presentation examines how academic libraries can support post-secondary ID. For context, the presentation first examines the state of post-secondary education for the ID with an analysis of data on the United States compiled by the ThinkCollege! Project. Geographic Information Systems (GIS) and statistical analysis will show regional and methodological trends in post-secondary support of the ID which currently lack any significant involvement by college libraries. Then, the presentation analyzes a case study of a book club at the Florida Atlantic University (FAU) libraries which has run for several years. Issues such as the selection of books, effective pedagogies, and evaluation procedures will be examined. The study has found that the instruction pedagogies used by libraries can be extended through concepts of Universal Learning Design (ULD) to effectively engage the ID. In particular, student-centered, participatory methodologies that accommodate different learning styles have proven to be especially useful. The choice of text is complex and determined not only by reading ability but familiarity of subject and features of the ID’s developmental trajectory. The selection of text is not only a necessity but also promises to give insight into the ID. Assessment remains a complex and unresolved subject, but the voluntary, sustained, and enthusiastic attendance of the ID is an undeniable indicator. The study finds that, through the traditional library vehicle of the book club, academic libraries can support ID students through training in both reading and socialization, two major goals of their post-secondary education.

Keywords: academic libraries, intellectual disability, literacy, post-secondary education

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Authors: Ferhati Ahmed, Fillali Ahmed, Oulhadj Younsi

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delimitation of the perimeters of protection in the catchment area of the city of Adrar, which are established around the sites for the collection of water intended for human consumption of drinking water, with the objective of ensuring the preservation and reducing the risks of point and accidental pollution of the resource (Continental Intercalar groundwater of the Northern Sahara of Algeria). This wellfield is located in the northeast of the city of Adrar, it covers an area of 132.56 km2 with 21 Drinking Water Supply wells (DWS), pumping a total flow of approximately 13 Hm3/year. The choice of this wellfield is based on the favorable hydrodynamic characteristics and their location in relation to the agglomeration. The vulnerability to pollution of this slick is very high because the slick is free and suffers from the absence of a protective layer. In recent years, several factors have been introduced around the field that can affect the quality of this precious resource, including the presence of a strong centre for domestic waste and agricultural and industrial activities. Thus, its sustainability requires the implementation of protection perimeters. The objective of this study is to set up three protection perimeters: immediate, close and remote. The application of the Wyssling method makes it possible to calculate the transfer time (t) of a drop of groundwater located at any point in the aquifer up to the abstraction and thus to define isochrones which in turn delimit each type of perimeter, 40 days for the nearer and 100 days for the farther away. Special restrictions are imposed for all activities depending on the distance of the catchment. The application of this method to the Adrar city catchment field showed that the close and remote protection perimeters successively occupy areas of 51.14 km2 and 92.9 km2. Perimeters are delimited by geolocated markers, 40 and 46 markers successively. These results show that the areas defined as "near protection perimeter" are free from activities likely to present a risk to the quality of the water used. On the other hand, on the areas defined as "remote protection perimeter," there is some agricultural and industrial activities that may present an imminent risk. A rigorous control of these activities and the restriction of the type of products applied in industrial and agricultural is imperative.

Keywords: continental intercalaire, drinking water supply, groundwater, perimeter of protection, wyssling method

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Authors: Shiuh-Kuang Yang, Sheam-Chyun Lin, Jyin-Wen Cheng, Deng-Guei Hsu

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The torsional mode of guided wave, T(0,1), has been applied to detect characteristics and defects in pipelines, especially in the cases of coated, elevated and buried pipes. The signals of minor corrosions would be covered by the noise, unfortunately, because the coated material and buried medium always induce a strong attenuation of the guided wave. Furthermore, the guided wave would be attenuated more seriously and make the signals hard to be identified when setting the array ring of the transducers on a general corrosion area of the pipe. The objective of this study is then to discuss the effects of the above-mentioned general corrosion on guided wave tests by experiments and signal processing techniques, based on the use of the finite element method, the two-dimensional Fourier transform and the continuous wavelet transform. Results show that the excitation energy would be reduced when the array ring set on the pipe surface having general corrosion. The non-uniformed contact surface also produces the unwanted asymmetric modes of the propagating guided wave. Some of them are even mixing together with T(0,1) mode and increase the difficulty of measurements, especially when a defect or local corrosion merged in the general corrosion area. It is also showed that the guided waves attenuation are increasing with the increasing corrosion depth or the rising inspection frequency. However, the coherent signals caused by the general corrosion would be decayed with increasing frequency. The results obtained from this research should be able to provide detectors to understand the impact when the array ring set on the area of general corrosion and the way to distinguish the localized corrosion which is inside the area of general corrosion.

Keywords: guided wave, finite element method, two-dimensional fourier transform, wavelet transform, general corrosion, localized corrosion

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Authors: Lian Zeng

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Double-Spear 1-H2-1 (DS1-H2-1) is an oncolytic virus and an innovative biological drug candidate. The chemical composition of the drug product is a live attenuated West Nile virus (WNV) containing the human T cell costimulator (CD86) gene. After intratumoral injection, the virus can rapidly self-replicate in the injected site and lyse/kill the tumor by repeated infection among tumor cells. We also established xenograft tumor models in mice to evaluate the drug candidate's efficacy on those tumors. The results from preclinical studies on transplanted tumors in immunodeficient mice showed that DS1-H2-1 had significant oncolytic effects on human-origin cancers: it completely (100%) shrieked human glioma; limited human neuroblastoma growth reached as high as 95% growth inhibition rate (%TGITW). The safety data of preclinical animal experiments confirmed that DS1-H2-1 is safe as a biological drug for clinical use. In the preclinical drug efficacy experiment, virus-drug administration with different doses did not show abnormal signs and disease symptoms in more than 300 tested mice, and no side effects or death occurred through various administration routes. Intravenous administration did not cause acute infectious disease or other side effects. However, the replication capacity of the virus in tumor tissue via intravenous administration is only 1% of that of direct intratumoral administration. The direct intratumoral administration of DS1-H2-1 had a higher rate of viral replication. Therefore, choosing direct intratumoral injection can ensure both efficacy and safety.

Keywords: oncolytic virus, WNV-CD86, immunotherapy drugs, glioma, neuroblastoma

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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